Validation of the Apple Watch for Heart Rate Variability Measurements during Relax and Mental Stress in Healthy Subjects - MDPI
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sensors
Article
Validation of the Apple Watch for Heart Rate
Variability Measurements during Relax and Mental
Stress in Healthy Subjects
David Hernando 1,2, * ID , Surya Roca 3 , Jorge Sancho 3 ID
, Álvaro Alesanco 3 ID
and Raquel Bailón 1,2
1 Biomedical Signal Interpretation & Computational Simulation (BSICoS) Group, Aragón Institute of
Engineering Research (I3A), IIS Aragón, University of Zaragoza, 50018 Zaragoza, Spain; rbailon@unizar.es
2 Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN),
28029 Madrid, Spain
3 Communications Networks and Information Technologies (CeNIT) Group, Aragón Institute of Engineering
Research (I3A), University of Zaragoza, 50018 Zaragoza, Spain; surya@unizar.es (S.R.);
jslarraz@unizar.es (J.S.); alesanco@unizar.es (Á.A.)
* Correspondence: dhernand@unizar.es; Tel.: +34-876-555462
Received: 4 July 2018; Accepted: 8 August 2018; Published: 10 August 2018
Abstract: Heart rate variability (HRV) analysis is a noninvasive tool widely used to assess autonomic
nervous system state. The market for wearable devices that measure the heart rate has grown
exponentially, as well as their potential use for healthcare and wellbeing applications. Still, there is a
lack of validation of these devices. In particular, this work aims to validate the Apple Watch in terms
of HRV derived from the RR interval series provided by the device, both in temporal (HRM (mean
heart rate), SDNN, RMSSD and pNN50) and frequency (low and high frequency powers, LF and HF)
domain. For this purpose, a database of 20 healthy volunteers subjected to relax and a mild cognitive
stress was used. First, RR interval series provided by Apple Watch were validated using as reference
the RR interval series provided by a Polar H7 using Bland-Altman plots and reliability and agreement
coefficients. Then, HRV parameters derived from both RR interval series were compared and their
ability to identify autonomic nervous system (ANS) response to mild cognitive stress was studied.
Apple Watch measurements presented very good reliability and agreement (>0.9). RR interval series
provided by Apple Watch contain gaps due to missing RR interval values (on average, 5 gaps per
recording, lasting 6.5 s per gap). Temporal HRV indices were not significantly affected by the gaps.
However, they produced a significant decrease in the LF and HF power. Despite these differences,
HRV indices derived from the Apple Watch RR interval series were able to reflect changes induced
by a mild mental stress, showing a significant decrease of HF power as well as RMSSD in stress with
respect to relax, suggesting the potential use of HRV measurements derived from Apple Watch for
stress monitoring.
Keywords: heart rate variability; wearable device; Apple Watch; RR series; validation; ANS assessment
1. Introduction
Heart rate variability (HRV) analysis is a widely accepted tool for the noninvasive assessment of
autonomic nervous system (ANS) [1]. Its analysis and use are becoming increasingly common, since it
is sensitive to both physiological and psychological changes [2]. Altered HRV measurements have
been reported in several diseases related to ANS dysregulation, including cardiovascular diseases,
such as ischemia, myocardial infarction and heart failure [1,3], metabolic diseases, such as diabetes and
Sensors 2018, 18, 2619; doi:10.3390/s18082619 www.mdpi.com/journal/sensorsSensors 2018, 18, 2619 2 of 11
obesity [3], and mental disorders, such as anxiety and depression [4,5]. Moreover, HRV measurements
have also been used to monitor sleep [6], stress [7,8], drowsiness [9] and exercise training [10–12].
Over the past 10 years, the market for health related wearable devices has grown exponentially,
as well as their potential use for healthcare and wellbeing applications. The most popular and
accepted ones, especially among the non-patient population, are the watch-like devices, which
usually provide average heart rate (HR) estimates, derived from the pulse signal recorded at the
wrist. They are based on photophethysmography (PPG), including a light source for illuminating
the skin and a photodetector, which measures the intensity of the reflected light. HR estimates are
based on the pulsatile changes in reflected light induced by fluctuations in blood flow every heartbeat.
Although technically feasible, only a small subset of these devices allows HRV measures. As pointed
out in [13], these devices usually lack from proper validation, limiting its applicability for enhancing
clinical care.
Different wearable devices have been evaluated and compared for HR estimation. In [13] the
Apple Watch achieved the best performance estimating HR at a one minute granularity, with median
error below 3% during sitting, walking, running, and cycling. However, neither of these devices has
been validated for HRV estimation. Although pulse rate variability (PRV) derived from PPG has been
validated as a surrogate of HRV derived from the electrocardiogram (ECG) [14], it is not the case for
PRV derived from the wrist. Only a few studies have addressed this issue, such as [15], where wrist
PPG-based HRV was evaluated for Emphatica E4 (Empatica Srl, Milano, Italy) and PulseOn (PulseOn
Oy, Espoo, Finland) devices, yielding reliable results only during sitting condition.
The objective of this study is to validate the Apple Watch for HRV measurement. Prior studies have
only validated aggregate HR estimates [13,16–19], but its outperformance over other devices [13,20]
makes it a potential candidate among wrist-worn devices for HRV measurement. Moreover, its spread
and acceptance are beating the wearable market [21]. The present study sets out the comparison of RR
intervals derived from Apple Watch using as reference the RR intervals derived from the Polar H7 chest
belt heart rate monitor (Polar Electro Oy, Kempele, Finland), which has been already validated [22,23].
Additionally, this study includes the validation of HRV measurements, derived from the RR intervals
from both devices, in two scenarios with different ANS balance: relax and metal stress.
2. Materials and Methods
2.1. Experimental Setup
A total of 20 volunteers agreed to participate in the study. All of them were apparently healthy
subjects. Written informed consent was obtained from each subject. The study protocol was approved
by the institutional ethics committee and was in accordance with the Declaration of Helsinki for
Human Research of 1974 (last modified in 2008).
Participants were asked to abstain from caffeine and smoking prior to the test and to only consume
a light meal 2 h prior to testing. The Polar H7 chest belt was placed tightly just below the chest muscles
and the Apple Watch was placed tightly as well in the left wrist. Both devices were cleaned up between
uses to avoid possible misreading effects. Measures were taken while participants were sitting in front
of a 21” computer screen, completing relax and mental stress test protocols. Instructions were given to
minimize left arm movements, where the Apple Watch was placed.
The experimental protocol consisted of two consecutive 5-min stages, namely relax and stress
stages, separated by a 1-min break. During the relax stage volunteers watched a relaxing video,
consisting in relaxing music and pleasant images, which was previously used in a study on emotion
recognition [24]. During the stress stage, participants performed an online version of the Stroop test
(www.onlinestrooptest.com), which is an attentional test in which participants see words (names of
different colors) written in colored ink and they are asked to name the color of the ink while ignoring
the meaning of the word.Sensors 2018, 18, 2619 3 of 11
Sensors 2018, 18, x 3 of 11
Raw RR data was obtained from Polar H7 ( ) using Elite HRV app installed in an Android
Raw RR data was obtained from Polar H7 (RR H7 ) using Elite HRV app installed in an Android
device. This app is able to export a text file containing the raw RR interval series with a precision of
device. This app is able to export a text file containing the raw RR interval series with a precision
milliseconds. On the other hand, the Breath app was used to extract RR interval series from Apple
of milliseconds. On the other hand, the Breath app was used to extract RR interval series from
Watch ( ). Breath app, developed by Apple, is the only way at this moment (watchOS 4.2
Apple Watch (RR to).obtain
operating system)AW
Breath app, developed by Apple, is the only way at this moment (watchOS
RR raw values since Apple does not include any programing method for
4.2developers
operating to system) to obtain RR raw
directly access the values
values. Thissince
app Apple
stores thedoesrawnotRR include anywith
values, programing method
a precision of
forcentiseconds,
developers in to the
directly
user’saccess the Health
Personal values.Record,
This app stores the
accessible raw
to be RR values,
exported in XMLwith a precision
format using of
centiseconds,
Apple’s Health in the
App. user’s Personal Health Record, accessible to be exported in XML format using
Apple’s Health App.
2.2. Synchronization and RR Matching
2.2. Synchronization and RR Matching
Figure 1 shows an example of both RR series, and . It can be seen that the RR series
Figurefrom
derived 1 shows an example
the Apple Watchofhasboth RR series,
some RR H7 andcorresponding
gaps, probably RR AW . It can to be time
seen instants
that the RR series
where
derived
Apple from
Watch the Apple Watch
proprietary has some
algorithms gaps,
have notprobably
been able corresponding
to reliably detectto time
the instants
PPG pulses. where Apple
These
Watch
gaps proprietary
were presentalgorithms have
in almost all ApplenotWatch
been able to reliably
recordings, withdetect the PPG
no apparent pulses.with
relation These
thegaps were
subject
present
or the in
testalmost all Apple
stage (relax WatchSynchronization
or stress). recordings, withbetween
no apparent relation
and withwas theperformed
subject orusing
the test
the delay
stage (relax that maximized
or stress). their cross correlation
Synchronization between RR using the first
H7 and RR AW 20 was
RR intervals,
performed where
usingnothe
gaps
delay
appeared
that maximized in Apple
theirWatch
cross recordings.
correlation All RR the
using intervals
first 20need
RRtointervals,
be matched wherefrom noboth
gaps devices: we in
appeared
createWatch recordings.
Apple by intentionally
All RRcreating
intervalsthe same
need to gaps in
be matched thanboth
from thosedevices:
in we. This way,
create RRboth
H7g by
intentionally creating the same gaps in RR H7 than those in RR AW . This way, both RR AW andeach
and have the same number of samples and all RR intervals are matched. For RR H7g
recording,
have the same the percentage
number of missing
of samples RRRR
and all intervals
intervals missed with respect
are matched. to the
For each total number
recording, of RR
the percentage
intervals were noted.
of missing RR intervals missed with respect to the total number of RR intervals were noted.
Figure1.1.Example
Figure Exampleof ofthe
the RR
RR series
series for
for aasubject
subjectininboth
bothstages:
stages:Polar
PolarH7H7
(blue) andand
(blue) Apple Watch
Apple (red).
Watch (red).
In the stress stage, there are gaps in the Apple Watch recording where no beats are
In the stress stage, there are gaps in the Apple Watch recording where no beats are detected. detected.
2.3. Validation of RR Series
2.3. Validation of RR Series
A Bland–Altman plot [25] was used to study the validity of the Apple Watch RR series, by
A Bland–Altman plot [25] was used to study the validity of the Apple Watch RR series, by
representing both and series. The bias, the limits of agreement (LOA, ±2*std values)
representing both RR paired
and the percentage ofH7g
andRR RRmeasurements
AW series. Theout
bias, the limits of agreement (LOA, ±2*std values)
of the LOA were also obtained.
and theTopercentage of paired RR measurements out of the
measure the interchangeability between both RR series, LOAtwowere also obtained.
reliability indices were used. First,
To measure the interchangeability between both RR series,
Lin’s concordance correlation coefficient (CCC) determines how far the observed two reliability indices
data deviate were
from the
used.
line ofFirst, Lin’s
perfect concordance
concordance line atcorrelation coefficient
45° on a square (CCC)
axis scatter plot determines
[26]. Second, how far the
intraclass observed
correlation
data deviate from the line of perfect concordance line at 45 ◦ on a square axis scatter plot [26].
coefficient (ICC) represents the ratio of between-sample variance and the total variance (between- and
Second, intraclass
within-sample) tocorrelation coefficient
measure precision under(ICC) represents
the model themarginal
of equal ratio of between-sample
distributions [27]. variance
Lastly, theand
the total variance (between- and within-sample) to measure precision under the model of equalSensors 2018, 18, 2619 4 of 11
marginal distributions [27]. Lastly, the agreement was measured by an information-based measure
of disagreement (IBMD), which is based on Shannon’s entropy [28]. This index equals 0 when the
observers agree (no disagreement); i.e., there is no information in the differences between both methods.
The agreement (A) can be quantified as A = 1−IBMD.
2.4. Heart Rate Variability Parameters
Heart rate variability parameters were divided in two classes: time and frequency domain indices.
Since both are calculated in different ways, the gaps also are addressed differently. For the temporal
domain analysis, we will derive HRV parameters from RR AW , RR H7 and RR H7g , the latter created
with simulated gaps as defined in Section 2.2. For the frequency domain analysis, most of the methods
used for power spectral density estimation require evenly sampled series, and interpolation is usually
employed to overcome the intrinsic unevenly sampled RR interval series. However, since gaps in
RR AW and RR H7g are too large (from 3.3 to 10.4 s), we first filled the gaps with artificial RR intervals,
created by linear interpolation. Note that, for simplicity, we use the same nomenclature, RR AW and
RR H7g , for temporal and frequency domain analysis, although they are slightly different: for temporal
HRV parameters, the gaps are omitted and the RR intervals are concatenated (RR AW and RR H7g are
shorter than RR H7 ), while for frequency HRV parameters the gaps are filled with artificially created
RR intervals (RR AW and RR H7g are the same length than RR H7 ).
For the temporal parameters, four parameters were proposed [1]: HRM, SDNN, RMSSD and
pNN50. HRM is the mean heart rate, which is obtained as the inverse of the mean heart period (mean
of the RR intervals). SDNN is the standard deviation of the NN intervals (or normal RR intervals),
which is a measure of the total power in the analyzed segment. A dispersion measure can be obtained
by calculating the root mean-square of successive differences of adjacent intervals (RMSSD). The last
parameter, pNN50, represents the percentage of pairs of adjacent intervals differing by more than
50 ms. RMSSD and pNN50 describe short-time variability. Note that whenever a gap appears in
RR AW , the corresponding intervals in RR H7 are also removed, and the remaining RR intervals are
concatenated. The difference of the intervals just before and after the gaps are not included in the
calculation of RMSSD and pNN50.
For the frequency domain analysis, the heart rate variability signal was obtained following a
method based on the integral pulse frequency modulation (IPFM) model described in [29]: from the
RR interval series, we derived the instantaneous heart rate signal, d HR (n), which is a continuous
and evenly sampled signal (sampled at 4 Hz). Then, we obtained the mean heart rate, d HRM (n),
using a low pass filter up to 0.03 Hz. The variability signal, d HRV (n), was obtained as d HRV (n) =
d HR (n) − d HRM (n). Finally, the modulating signal, which is assumed to represent the ANS modulation
on the sinoatrial node, was obtained as m̂(n) = d HRV (n)/d HRM (n) [30].
The Welch periodogram was applied to estimate the spectral properties of the HRV signals
m̂ AW (n), m̂ H7 (n) and m̂ H7g (n) (derived from RR AW , RR H7 and RR H7g , respectively), using a
Hamming window with a length of 150 s with an overlap of 120 s. Low frequency (LF) and high
frequency (HF) powers, PLF and PHF respectively, were obtained integrating the power in their bands
(LF: 0.04–0.15 Hz, HF: 0.15–0.4 Hz) [1].
2.5. Statistical Analysis
None of all HRV parameters followed a normal distribution (tested with a Kolmogorov test).
To compare HRV parameters, a paired Wilcoxon test was applied. Then we performed 3 different
comparisons: (1) we analyzed the influence of the gaps by comparing HRV parameters derived from
RR H7 and RR H7g ; (2) we compared HRV parameters derived from RR AW and RR H7g ; and (3) HRV
parameters derived from RR AW were compared in relax vs. stress stage to evaluate the ability of those
parameters to reflect ANS response to stress, using changes in HRV parameters derived from RR H7
as the reference ANS response. The difference was considered to be significantly different from zero
when p < 0.05.Sensors 2018, 18, 2619 5 of 11
Distribution of HRV parameters are shown using boxplots: on each box, the central mark indicates
the median, and the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively.
The whiskers extend
Sensors 2018, 18, x to the most extreme data points not considered outliers, and the outliers 5 of 11 are
plotted individually using the ‘+’ symbol. An outlier is defined as a point that falls more than 1.5
respectively. The whiskers extend to the most extreme data points not considered outliers, and the
times the interquartile range (difference between 75th and 25th percentiles) above the 75th percentile
outliers are plotted individually using the ‘+’ symbol. An outlier is defined as a point that falls more
or below the1.5
than 25th percentile.
times the interquartile range (difference between 75th and 25th percentiles) above the 75th
percentile or below the 25th percentile.
3. Results
3. Results
3.1. Validity of RR Series
3.1. Validity of RR Series
A total of 206 gaps were found in the Apple Watch recordings, equivalent to 1321 missing RR
A total10%
intervals (around of 206
ofgaps
totalwere found in
intervals). Ontheaverage,
Apple Watch
each recordings, equivalent5 to
recording presents 1321with
gaps, missing RR of
a length
intervals (around 10% of total intervals). On average, each recording presents
6 s per gap. The minimum length found was 3.3 s and the maximum length was 10.4 s. No differences5 gaps, with a length
of 6 s per gap. The minimum length found was 3.3 s and the maximum length was 10.4 s. No
in the number or length of these gaps were found between relax and stress recordings.
differences in the number or length of these gaps were found between relax and stress recordings.
Figure 2 shows the Bland–Altman plot which evaluates the RR interval discrepancies between
Figure 2 shows the Bland–Altman plot which evaluates the RR interval discrepancies between
Polar H7 andH7
Polar Apple Watch
and Apple measurements
Watch measurements and the
and thestability
stability across thedifferent
across the different values
values of intervals.
of the the intervals.
A total A
of total
12,109 pairedpaired
of 12,109 RR intervals
RR intervalswere used
were from
used fromboth
bothrelax andstress
relax and stressstages.
stages.
TheThe
biasbias (central
(central
horizontal line) was
horizontal line) 0.06 ms.ms.
was 0.06 TheThe LOA
LOA(upper andlower
(upper and lower lines)
lines) contained
contained 96.21%96.21% of the intervals.
of the intervals. No
visual
No visual differences
differences werefound
were found inin the
the discrepancies
discrepancies between
between lower and and
lower higher RR intervals.
higher RR intervals.
Figure 2. Bland-Altman plot: vs. . Mean of the difference of the RR series ±2*std values
Figure 2. Bland-Altman plot: RR H7g vs. RR AW . Mean of the difference of the RR series ±2*std values
(limits of agreement, LOA).
(limits of agreement, LOA).
Table 1 shows the mean values of the RR intervals for each stage, as well as the reliability and
agreement
Table 1 shows indices. Whilevalues
the mean slightlyoflower
the RRin the stress stage,
intervals for eachthesestage,
indicesasshow
wellexcellent reliability and
as the reliability
and indices.
agreement agreement between
While the measurements
slightly lower in the in both stage,
stress stages. these
Bias and LOA are
indices showalsoexcellent
shown forreliability
relax and and
agreement between the measurements in both stages. Bias and LOA are also shown for relaxshows
stress stages, which are very similar to those obtained with the combined data. The last row and stress
the mean of the percentage of missing RR intervals in .
stages, which are very similar to those obtained with the combined data. The last row shows the mean
of the percentage of missing
Table 1. RR intervals
Validity in RR
of RR series .
AWApple
from Watch. CI = Confidence interval.
RELAXCI = Confidence
Table 1. Validity of RR series from Apple Watch. STRESS
interval.
Mean (SD) H7 RR intervals (ms) 869.28 (114.01) 834.78 (97.43)
Mean (SD) AW RR intervals (ms) 869.23
RELAX(114.39) 834.70
STRESS (97.84)
CCC (90% CI)
Mean (SD) H7 RR intervals (ms) 0.989 (0.981,
869.28 (114.01) 0.998) 0.977 (0.970,
834.78 (97.43)0.985)
ICC (90% CI)
Mean (SD) AW RR intervals (ms) 0.989 (0.984,
869.23 (114.39) 0.996) 0.982 (0.977,
834.70 (97.84)0.987)
CCCA (90%
(90%CI)
CI) 0.989 (0.981,
0.993 0.998)
(0.987, 0.999) 0.977
0.983(0.970,
(0.975,0.985)
0.991)
ICC (90% CI) 0.989 (0.984, 0.996) 0.982 (0.977, 0.987)
Bias (Out LOA) 0.049 (3.29%) 0.078 (3.93%)
A (90% CI) 0.993 (0.987, 0.999) 0.983 (0.975, 0.991)
BiasMean (SD)% missed RR intervals 0.04910.98
(Out LOA) (9.78)
(3.29%) 9.45(3.93%)
0.078 (7.30)
Mean (SD)% missed RR intervals 10.98 (9.78) 9.45 (7.30)Sensors 2018, 18, x 6 of 11
Sensors 2018, 18, x 6 of 11
Sensors
3.2. HRV 18, 2619
2018,Parameters:
Temporal Domain 6 of 11
3.2. HRV Parameters: Temporal Domain
Figure 3 shows the temporal parameters for both relax and stress stages. No significant
3.2. Figure
HRV 3 shows
Parameters:
differences were the
Temporal
found temporal
in any Domainparameters
parameter for both relaxand
neither between and stress, nor
stages. No significant
between and
differences
. were found in any parameter neither between and , nor between and
Figure 3 shows the temporal parameters for both relax and stress stages. No significant differences
.
were found in any parameter neither between RR H7 and RR H7g , nor between RR H7g and RR AW .
Figure 3.
Figure 3. Heart
Heart rate
rate variability
variability (HRV)
(HRV) parameters:
parameters: time
time domain.
domain.
Figure 3. Heart rate variability (HRV) parameters: time domain.
3.3. HRV Parameters: Frequency Domain
3.3. HRV Parameters: Frequency Domain
3.3. HRV Parameters: Frequency Domain
Figure 4 shows the frequency HRV parameters derived from , and in relax
Figure 4 shows the frequency HRV parameters derived from RR H7 , RR H7g and RR AW in
and Figure 4 shows
stress stages. the frequency
Both and HRV parameters
show significantderived from(marked
differences , with anand in relax
asterisk between
relax and stress stages. Both PLF and PHF show significant differences (marked with an asterisk
and stress stages.
the boxplots) in Both whenandcomparing
show to
significant differences
, being (marked
lower in with anofasterisk
the presence between
the gaps. When
between the boxplots) in RR H7g when comparing to RR H7 , being lower in the presence of the gaps.
the boxplots)HRV
comparing in parameters
when comparing
derived to
from , being
and lower in, neither
the presence nor
of the gaps. When
presented
When comparing HRV parameters derived from RR AW and RR H7g , neither PLF nor PHF presented
comparing HRV parameters derived from
significant differences.
differences. and , neither nor presented
significant
significant differences.
Figure 4. HRV parameters: frequency domain, derived from RR H7 , RR H7g and RR AW . * denotes
Figure 4. HRV parameters: frequency domain, derived from , and . * denotes
significant differences (p < 0.05) between adjacent boxplots. Adim refers to adimensional units.
Figure 4. HRV
significant parameters:
differences frequency
(p < 0.05) betweendomain,
adjacentderived from
boxplots. , to adimensional
Adim refers and .units.
* denotes
significant differences (p < 0.05) between adjacent boxplots. Adim refers to adimensional units.Sensors 2018, 18, x 7 of 11
Sensors 2018, 18,
Sensors 2018, 18, 2619
x 7 of 11
7 of 11
3.4. HRV Parameters: Relax vs. Stress
3.4. HRV Parameters: Relax vs. Stress
Figures
3.4. HRV 5 and 6Relax
Parameters: display HRV parameters derived from
vs. Stress and in relax vs. stress
stages. All temporal
Figures 5 and 6HRV parameters
display (HRM, SDNN,
HRV parameters RMSSD
derived from and pNN50)
and presented changes
in relax from
vs. stress
Figures 5 and 6 display HRV parameters derived from RR AW and RR H7 in relax vs. stress stages.
relax toAll
stages. stress in bothHRV
temporal Apple Watch and
parameters PolarSDNN,
(HRM, H7 recordings:
RMSSD an andincrease
pNN50)inpresented
HRM andchanges
a decrease
fromin
All temporal HRV parameters (HRM, SDNN, RMSSD and pNN50) presented changes from relax to
the rest
relax to of parameters.
stress We observed
in both Apple a decrease
Watch and Polar H7in recordings:
in stressan
with respect
increase into relaxand
HRM stage, being only
a decrease in
stress in both Apple Watch and Polar H7 recordings: an increase in HRM and a decrease in the rest of
statistically
the significantWe
rest of parameters. in Polar H7 recordings
observed a decrease (p
in = 0.030 for Polar
in stress withH7, p = 0.065
respect for stage,
to relax Applebeing
Watch). A
only
parameters. We observed a decrease in PLF in stress with respect to relax stage, being only statistically
significant decrease
statistically was
significant infound
Polar H7in both devices(pwhen
recordings comparing
= 0.030 for Polar H7,. p = 0.065 for Apple Watch). A
significant in Polar H7 recordings (p = 0.030 for Polar H7, p = 0.065 for Apple Watch). A significant
significant decrease was found in both devices when comparing .
decrease was found in both devices when comparing PHF .
Figure 5.
Figure 5. HRV
HRVparameters:
parameters:temporal
temporaldomain
domain (Relax
(Relax vs.vs. Stress).
Stress). * denotes
* denotes significant
significant differences
differences (p <
(p < 0.05)
Figure
0.05) between
5. HRV
between adjacent
parameters:
adjacent boxplots.
temporal domain (Relax vs. Stress). * denotes significant differences
boxplots.
(p < 0.05) between adjacent boxplots.
Figure 6. HRV parameters: frequency domain (Relax vs. Stress). * denotes significant differences
Figure
(p 6. between
< 0.05) HRV parameters: frequencyAdim
adjacent boxplots. domain (Relax
refers vs. Stress). * denotes
to adimensional units. significant differences (p <
0.05) between
Figure 6. HRV adjacent boxplots.
parameters: Adimdomain
frequency refers to adimensional
(Relax units.
vs. Stress). * denotes significant differences (p <
0.05) between adjacent boxplots. Adim refers to adimensional units.Sensors 2018, 18, 2619 8 of 11
4. Discussion
This is the first study to validate HRV measurements using an Apple Watch to the authors’
knowledge. Many of the wrist-worn devices aimed at assessing well-being, stress, and fitness provide
average HR values, derived from a pulse signal, instead of HRV. Fitbit, Jawbone, TomTom, Garming,
Withing, Samsumg, among others, commercialize such devices. However, although changes in
average HR are mainly induced by ANS and can be significantly different in some physio-pathological
situations, it cannot be considered a measure of autonomic function [3,31]. Moreover, there are
situations where altered ANS function is manifested in HRV but not in HR changes, such as in
depressed patients with respect to controls [32] or in exercise contexts [33,34].
In order to validate HRV measurements derived from an Apple Watch, in this work an
experimental study was conducted on young healthy volunteers subjected to mild relax and stress.
HRV measurements derived from Apple Watch were compared to those obtained from a validated
heart rate monitor, in particular Polar H7 chest band. Several works in the literature have already
validated, using as reference the RR interval series derived from a synchronous ECG, the RR interval
series given by different models of Polar devices: S810 [35,36], RS800 [37,38], and V800 with a H7
chest belt [22,23].
Regarding the RR intervals series, which are the basis for any HRV analysis, the bias was almost 0
and most pairs of RR intervals (around 96%) were within the limits of agreement in the Bland Altman
plot. These results are consistent when studying both stages separately. Moreover, the discrepancy
between Apple Watch and Polar H7 is similar with no dependency of the heart rate. The RR series
present excellent reliability and agreement when analyzing matched pairs of RR intervals. These results
agree with other studies for Apple Watch [17,18] and other heart rate watches [13,16,19].
However, it is important to note the existence in Apple Watch measurements of missing beats.
Around 10% of the beats could not be detected by the Apple Watch in both the relax and stress stages.
This could be not problematic if they were isolated beats, but they usually were consecutive beats,
which lead to gaps in the RR series. The shorter gaps were longer than 3 s, being easily identifiable as
abnormally large RR intervals. The origin of these gaps could be varied, from bad skin contact to fast
arm movement, but ultimately, we cannot conclude anything about these gaps because we do not have
direct access to the algorithms used by the Apple Watch. In [18], they also reported missing values
in the Apple Watch, with the proportion of heart rate values actually measured by the Apple Watch
decreasing with increasing exercise intensity. They also reported that these missing heart rate values
were higher in the first minute of exercise (between 20 and 40% of RR intervals), particularly at higher
intensities of exercise. In our data, however, we did not find more missing beats in the stress stage
compared to the relax stage, and the total missing RR intervals was about 10% of total intervals.
These missing beats are important when extracting HRV parameters in the frequency domain,
since the RR interval series needs to be interpolated in the gaps. This might influence HRV frequency
parameters. To study the influence of these gaps in frequency parameters PLF and PHF , we created
RR H7g by intentionally creating gaps in the RR H7 series, removing the missing RR intervals found
in RR AW . This analysis showed that both PLF and PHF were significantly lower in the presence of
simulated gaps. However, when comparing HRV frequency parameters derived from RR AW and from
RR H7g , no significant difference was found, suggesting that differences between HRV parameters
derived from RR series provided by Apple Watch and Polar H7 are indeed due to the presence of gaps
in the Apple Watch series, rather than to the different signal from which they are derived or to the
different time resolution. The effect of gaps on HRV temporal parameters was also investigated, but no
significant differences were found, suggesting that these parameters are less sensitive to the presence
of gaps.
Despite these differences, HRV measurements derived from Apple Watch were still able to
reflect the ANS response induced by the stress stage, replicating most of the trends observed in HRV
measurements derived from the Polar H7. Induced mental stress resulted in a significant decrease
in PHF with respect to relax (in both the Apple Watch and Polar H7 measures, note that there wereSensors 2018, 18, 2619 9 of 11
no gaps in the Polar H7 recordings), suggesting the inhibition of parasympathetic stimulation, as
already reported in numerous studies [8,39]. A similar conclusion can be extracted with the decrease in
RMSSD parameter in the stress phase, being this parameter associated to the parasympathetic activity.
Although not shown in the results, the ratio between LF and HF powers (R), as well as the normalized
LF power (PLFn ), were also obtained. Comparing relax and stress, these parameters have shown in
other studies a significant increase during stress [8], which means a predominance of the sympathetic
activity. In this work, however, the increase found in R and PLFn was not significant even in the Polar
H7 measures, possibly due to the fact that the level of stress was moderate: see the moderately low
maximum HR achieved in the stress stage (around 100 bpm). Still, the significant decrease in PHF was
evident using both the Polar H7 and the Apple Watch, supporting the potential use of the Apple Watch
for stress monitoring.
However, one of the main limitations of using the Apple Watch to obtain and use HRV measures
is that, at this moment, the RR series is only available through the use of the Apple Breath App
in an indirect way (exporting the measured values using the Health App, where all Health-related
data from the user is stored). Apple does not provide within the watch OS 4.2 SDK any function to
access raw RR measures. Thus, developers are not able to develop an App that takes advantage of
the potential shown by Apple Watch in HRV monitoring. Besides, Breath App stops when repeated
motion is detected. This does not allow us to validate HRV measurements in low or moderate exercise.
Nevertheless, this SDK limitation can be removed by Apple in a further SDK release. This inclusion
would leverage the development of Apps able to monitoring stress conditions, among other things, by
the developer’s community.
5. Conclusions
Heart rate variability parameters extracted from an Apple Watch device have been validated using
a Polar H7 band as a reference during relax and mental stress in 20 healthy volunteers. Reliability and
agreement coefficients were computed for the RR interval series provided by both devices in relax and
stress stages, achieving very good results (reliability and agreement > 0.9). No significant differences
were found when comparing temporal HRV parameters (SDNN, RMSSD, pNN50 and HRM) derived
from the RR interval series provided by both devices. However, frequency HRV parameters LF and
HF powers were significantly different when derived from the Apple Watch, due to the appearance of
gaps in the RR series, both in relax and stress stages. Nonetheless, a decrease in the HF power (and in
the RMSSD parameter) was observed in stress with respect to relax stage when using both devices,
which supports the potential use of the Apple Watch for stress monitoring.
Author Contributions: Conceptualization, D.H., Á.A. and R.B.; Methodology, D.H. and R.B.; Formal Analysis,
D.H.; Data Curation, S.R., J.S. and Á.A.; Writing-Original Draft Preparation, D.H., S.R., J.S., Á.A. and R.B.;
Writing-Review & Editing, D.H., Á.A. and R.B.; Supervision, Á.A. and R.B.
Funding: This work was supported by Centro de Investigación Biomédica en Red–Bioingeniería, Biomateriales y
Nanomedicina (CIBER-BBN) through Instituto de Salud Carlos III, by the Ministerio de Economía, Industria y
Competitividad, Gobierno de España, European Regional Development Fund (TIN2016-76770-R) and FEDER
“Construyendo Europa desde Aragón” (Grupo de investigación T31_17R), by Aragón Government through Grupo
de Referencia BSICoS (T39_17R), by Aragón Institute of Engineering Research (I3A), IIS Aragón and European
Social Fund (EU).
Acknowledgments: The computation was performed by the ICTS “NANBIOSIS”, more specifically by the
High Performance Computing Unit of the CIBER-BBN at the University of Zaragoza. The authors also want to
acknowledge the support from Ogilvy Paris.
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the
study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to
publish the results.Sensors 2018, 18, 2619 10 of 11
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